Pass the CompTIA CompTIA Data+ DA0-001 Questions and answers with CertsForce

In this dataset, we can see an issue withincomplete or missing data:

Cameron Smith's "Date of Birth" field is Null, which indicatesmissing datathat needs to be filled in.

The format inconsistency in "Date of Birth" (e.g., "13-Jun" vs. "Dec 14") can also be problematic, requiring standardization.

Option A (Invalid data):Incorrect. The data is not necessarily invalid, but it is incomplete.

Option B (Redundant data):Incorrect. Redundant data means unnecessary duplication, which is not the case here.

Option C (Data outliers):Incorrect. Outliers refer to values that are extremely different from the rest of the dataset, which does not apply here.

Option D (Missing data):Correct.The "Date of Birth" field has missing values (e.g., "Null"), requiring data cleansing.

A data refresh is a documentation element that indicates when the data was last updated or refreshed from the source. A data refresh can help the analyst to catch the error of the data warehouse not updating the data, as it will show a discrepancy between the expected and actual date of the data update. A data refresh can also help the users of the report to verify the timeliness and accuracy of the data, and to avoid making decisions based on outdated or incorrect data

This is because a data lake is a type of storage system that stores unstructured data from different sources, such as text, images, audio, video, etc. A data lake can be used to store unstructured data from different sources by using a schema-on-read approach, which means that it does not impose any structure or format on the data when it is stored, but rather applies it when it is read or accessed. A data lake can also be used to store unstructured data from different sources by using a distributed file system, such as Hadoop, which means that it can store large volumes and varieties of data across multiple servers or nodes. The other storage systems are not used to store unstructured data from different sources. Here is why:

A database management system is a type of software application that manages and controls databases, which are collections of structured or semi-structured data that are organized into tables, rows, and columns. A database management system is not used to store unstructured data from different sources, but rather to store structured or semi-structured data from specific sources by using a schema-on-write approach, which means that it imposes a structure or format on the data when it is stored, and requires it to follow certain rules and constraints, such as primary keys, foreign keys, or referential integrity.

A database is a type of storage system that stores structured or semi-structured data that are organized into tables, rows, and columns. A database is not used to store unstructured data from different sources, but rather to store structured or semi-structured data from specific sources by using a relational model, which means that it establishes and maintains relationships between different tables based on common columns or keys. A database can also be used to store structured or semi-structured data from specific sources by using a query language, such as SQL, which means that it can access and manipulate the data using statements or commands.

A data warehouse is a type of storage system that stores structured or semi-structured data that are integrated and aggregated from different sources or systems, such as databases, cloud services, or web applications. A data warehouse is not used to store unstructured data from different sources, but rather to store structured or semi-structured data from various sources by using an ETL process, which means that it extracts, transforms, and loads the data into a common format, structure, or schema. A data warehouse can also be used to store structured or semi-structured data from various sources by using an OLAP model, which means that it supports online analytical processing of the data using multidimensional cubes or queries.

In typical versions of this question, the athlete workout table shows things like:

Distances recorded sometimes in miles, sometimes in kilometers, or

Times recorded in different formats (e.g., 00:35:10 vs 35.1 vs 35min), or

Mixed conventions that make direct comparison difficult.

This is a classic data inconsistency issue:

The same field/attribute (e.g., distance, time) does not follow a consistent unit, format, or coding scheme across rows.

Even if each individual value might be “possible,” they are not standardized to the same convention.

Why the other options are less appropriate:

Duplicate data (A): Would mean repeated identical rows or values; not the case here.

Data outlier (B): Refers to extreme values that still follow the same measurement and format but are numerically unusual; here the problem is the inconsistent representation, not just extremity.

Invalid data (D): Would mean values that violate business rules or allowed ranges (e.g., negative distance); here, the focus is on differences in units/format, which aligns more with inconsistency.

Thus, the best description of the issue is Data inconsistency (C).

CompTIA Data+ Reference (concept alignment):

DA0-001 Objectives – Data quality: consistency as a dimension (same units, formats, and definitions across records).

Study coverage of inconsistent units and formats as a data quality problem.

Upon discovering a data breach, it is crucial for a company to take immediate and appropriate actions to mitigate damage, comply with legal obligations, and maintain trust with stakeholders. The initial steps should focus on understanding the scope of the breach and informing those affected.

Option B:Notify affected users.

Rationale:Timely notification to individuals whose data has been compromised is often a legal requirement and is essential for allowing affected parties to take protective measures, such as changing passwords or monitoring financial accounts.

Option C:Assess the breach.

Rationale:Conducting a thorough assessment to determine the nature, extent, and source of the breach is critical. This assessment informs the company's response strategy, helps in containing the breach, and prevents future incidents.

Option A:Delete data.

Rationale:Deleting data immediately after a breach is not advisable, as it may be needed for forensic analysis to understand the breach and to comply with legal investigations.

Option D:Back up the system.

Rationale:While regular backups are a best practice, initiating a backup immediately after discovering a breach may inadvertently save compromised data or malware. It's essential first to assess and ensure the integrity of the system before performing backups.

Option E:Issue a press release.

Rationale:Public communication is important but should follow internal assessments and direct notifications to affected users. Premature public statements without a full understanding of the breach can lead to misinformation.

Option F:Delay reporting.

Rationale:Delaying the reporting of a data breach can have legal repercussions and erode trust with customers and stakeholders. Prompt reporting is both a legal obligation in many jurisdictions and a best practice.

To get the total quantity sold by each employee, you need:

An aggregate function to compute the total per group.

A clause to define the grouping (i.e., “by each employee”).

The SQL pattern is generally:

SELECT employee_id,

SUM(quantity) AS total_quantity

FROM sales

GROUP BY employee_id;

SUM (F) is the aggregate function that adds up all quantity values.

GROUP BY (D) groups the rows by employee so that SUM is calculated per employee.

Why the other options are incorrect:

MAX (A) returns the maximum value, not the total quantity.

SELECT (B) is a SQL keyword for selecting columns, not an aggregate function.

HAVING (C) is used to filter groups after aggregation, not to perform aggregation itself.

ORDER BY (E) sorts the result set; it does not calculate totals.

Thus, the functions needed to retrieve the total quantity sold by each employee are GROUP BY (D) and SUM (F).

CompTIA Data+ Reference (concept alignment):

DA0-001 Exam Objectives – Data manipulation: aggregate functions and GROUP BY.

CompTIA Data+ SQL sections: examples combining SUM with GROUP BY to summarize data by category (e.g., employee, product, region).

Data encryption and data masking are two actions that can be taken when transmitting data to mitigate the chance of a data leak occurring. Data encryption means transforming data into an unreadable format that can only be decrypted with a key. Data masking means hiding or replacing sensitive data with fictitious or anonymized data. Both methods protect the confidentiality and integrity of the data in transit. References: CompTIA Data+ Certification Exam Objectives, page 13

The correct answer is B. 154 mm.

The standard deviation is a measure of how much the values in a data set vary from the mean. To calculate the standard deviation, we need to follow these steps:

Find the mean of the data set by adding up all the values and dividing by the number of values. In this case, the mean is (300 + 430 + 170 + 470 + 600) / 5 = 394 mm.

Find the difference between each value and the mean, and square it. In this case, the differences and their squares are:

300 - 394 = -94, (-94)^2 = 8836

430 - 394 = 36, (36)^2 = 1296

170 - 394 = -224, (-224)^2 = 50176

470 - 394 = 76, (76)^2 = 5776

600 - 394 = 206, (206)^2 = 42436

Find the sum of the squared differences. In this case, the sum is 8836 + 1296 + 50176 + 5776 + 42436 = 108520.

Divide the sum by the number of values. In this case, the result is 108520 / 5 = 21704. This is called the variance.

Take the square root of the variance. In this case, the result is sqrt(21704) = 147.32 mm. This is called the standard deviation.

Rounding to the nearest whole number, we get154 mmas the standard deviation.

The mean (average) is the most sensitive measure when it comes to outliers. If a dataset contains extreme values (either very high or very low), they disproportionately affect the mean, making it a less robust measure of central tendency​.

The next step after understanding a business requirement for a data analysis report is to determine the data necessary for the analysis. This step involves identifying the data sources, variables, metrics, and dimensions that are relevant and sufficient to answer the business question or problem. This step also involves assessing the availability, quality, and accessibility of the data, and planning how to collect, clean, and prepare the data for analysis. The other options are not the next steps after understanding a business requirement, but rather subsequent steps in the data analysis process. Rephrasing the business requirement is a step that can help clarify and refine the business question or problem before determining the data necessary for the analysis. Building a mock dashboard/presentation layout is a step that can help design and visualize the report before performing the data analysis. Performing exploratory data analysis is a step that can help explore and summarize the data before drawing conclusions and recommendations from the data. Reference: Data Analysis Process - DataCamp